滾動軸承故障特征提取與早期診斷方法研究
發(fā)布時間：2018-11-05 14:20
【摘要】：滾動軸承是旋轉(zhuǎn)機(jī)械中廣泛應(yīng)用的零部件之一,其安全運(yùn)行與否對整個設(shè)備有至關(guān)重要的影響。本文以滾動軸承為研究對象,在總結(jié)滾動軸承現(xiàn)有故障診斷技術(shù)的基礎(chǔ)上,采用現(xiàn)代信號處理技術(shù),對滾動軸承的特征提取、早期故障檢測和早期故障位置識別展開研究工作。本文的主要研究內(nèi)容如下:(1)針對滾動軸承不同故障類型的振動信號所呈現(xiàn)的復(fù)雜度不同的特點(diǎn),提出了基于多尺度符號動力學(xué)熵(MSDE)的滾動軸承故障位置特征提取方法。符號動力學(xué)熵在符號動力學(xué)濾波(SDF)的基礎(chǔ)上,構(gòu)建符號序列的狀態(tài)模式矩陣和狀態(tài)遷移矩陣,極大保留振動信號的故障信息。經(jīng)過四種仿真信號測試,驗(yàn)證了符號動力學(xué)熵能夠有效辨別信號動力學(xué)特性變化,并具有較高的計算效率。結(jié)合多尺度分析的概念,提出了MSDE的特征提取方法,使其能夠在不同尺度下描述信號的復(fù)雜度。同時采用最大相關(guān)最小冗余進(jìn)行特征優(yōu)選和最小二乘支持向量機(jī)進(jìn)行模式識別,經(jīng)實(shí)驗(yàn)數(shù)據(jù)測試,基于MSDE的特征提取方法具有較高的識別精度和計算效率,實(shí)現(xiàn)了滾動軸承不同故障位置的準(zhǔn)確區(qū)分。(2)針對多尺度模糊熵(MFE)對滾動軸承故障損傷程度特征提取能力不足的問題,提出了基于層次模糊熵(HFE)的特征提取方法。針對MFE中多尺度分析的缺點(diǎn),提出了基于HFE的滾動軸承特征提取方法。層次分析能夠同時提取信號中低頻部分和高頻部分隱藏的故障信息。通過白噪聲和粉紅噪聲的測試,HFE能夠更準(zhǔn)確、全面描述時間序列復(fù)雜度的變化,且具有更好的穩(wěn)定性。然后采用拉普拉斯分值對特征進(jìn)行優(yōu)選,并結(jié)合二叉樹支持向量機(jī)實(shí)現(xiàn)滾動軸承不同故障程度的識別。同MFE方法進(jìn)行對比分析表明基于HFE所提取的特征向量具有良好的可分性,實(shí)現(xiàn)了滾動軸承不同故障程度的準(zhǔn)確區(qū)分。(3)為實(shí)現(xiàn)滾動軸承的早期故障檢測,提出了基于SDF的性能監(jiān)測指標(biāo)M。監(jiān)測指標(biāo)M的計算過程不依賴于軸承的故障樣本數(shù)據(jù),且與有效值和峭度因子相比,監(jiān)測指標(biāo)M對軸承早期故障具有較高的敏感性和穩(wěn)定性。將監(jiān)測指標(biāo)M與累積和相結(jié)合來完成軸承早期故障的報警,經(jīng)兩次軸承加速全壽命實(shí)驗(yàn)的測試,該方法能夠有效地檢測出軸承的早期故障。(4)針對滾動軸承早期故障特征極其微弱的問題,提出了基于內(nèi)稟特征尺度分解(ICD)與優(yōu)化品質(zhì)因子的共振稀疏分解(ORSSD)的滾動軸承早期故障診斷方法。為了降低噪聲的干擾,在總結(jié)局部均值分解(LMD)方法和本征時間尺度分解(ITD)方法優(yōu)勢的基礎(chǔ)上,提出了ICD分解方法。以仿真信號為例,比較了ICD和LMD的分解性能。分析結(jié)果表明ICD具有更高的分解精度和計算效率,更加適合于分解滾動軸承的振動信號。針對共振稀疏分解中品質(zhì)因子Q難以確定的問題,引入了特征頻率比(CFR)進(jìn)行品質(zhì)因子Q的優(yōu)選,根據(jù)最大的CFR值找出最優(yōu)的高品質(zhì)因子QH和低品質(zhì)因子QL。將ICD與ORSSD相結(jié)合,提出了一種基于ICD和ORSSD的滾動軸承早期故障位置的識別方法。經(jīng)仿真與實(shí)驗(yàn)信號測試,該方法能夠有效提取早期故障特征,完成滾動軸承的早期故障診斷。
[Abstract]:Rolling bearing is one of the widely used components in rotating machinery, and its safe operation is of vital importance to the whole equipment. On the basis of summarizing the existing fault diagnosis technology of rolling bearing, this paper uses modern signal processing technology to study the feature extraction, early fault detection and early fault location identification of rolling bearing. The main contents of this paper are as follows: (1) The feature extraction method based on multi-scale symbol dynamic entropy (MSDE) based on multi-scale symbol dynamic entropy (MSDE) is presented in this paper. Based on the symbol dynamic filtering (SDF), the symbol dynamic entropy constructs a state pattern matrix and a state transition matrix of the symbol sequence, thereby greatly preserving the fault information of the vibration signal. Through the four simulation signal tests, it is verified that the symbolic dynamics entropy can effectively distinguish the change of signal dynamics and has higher computational efficiency. According to the concept of multi-scale analysis, a feature extraction method of MSDE is proposed, which can describe the complexity of signal at different scales. At the same time, the maximum correlation minimum redundancy is used to carry out pattern recognition and the least two-multiplication support vector machine to carry out pattern recognition, and the method has higher recognition precision and calculation efficiency based on the feature extraction method of the MSDE and realizes accurate discrimination of different fault positions of the rolling bearing. (2) Aiming at the problem of insufficient feature extraction ability of multi-scale fuzzy entropy (MFE) on fault damage degree of rolling bearing, a method of feature extraction based on hierarchical fuzzy entropy (HFE) is proposed. Aiming at the disadvantage of multi-scale analysis in MFE, a method for feature extraction of rolling bearing based on HFE is proposed. The hierarchical analysis can extract fault information hidden from low and high frequency parts of the signal at the same time. Through the test of white noise and pink noise, HFE can describe the change of time sequence complexity more accurately and comprehensively, and has better stability. then the characteristic is optimized by using the Laplacian value, and the recognition of different degrees of fault of the rolling bearing is realized by combining the binary tree support vector machine. Compared with MFE method, it is shown that HFE has good separability based on the feature vector extracted by HFE, which realizes the accurate differentiation of different fault degree of rolling bearing. (3) In order to realize the early fault detection of rolling bearing, the performance monitoring index M based on SDF is put forward. The calculation process of the monitoring index M is independent of the fault sample data of the bearing, and compared with the effective value and the kurtosis factor, The monitoring index M has high sensitivity and stability to early bearing failure. The monitoring index M is combined with the accumulation and the combination to complete the early fault alarm of the bearing, and the test of the full life experiment is accelerated through two bearings, and the method can effectively detect the early failure of the bearing. (4) The early fault diagnosis method of rolling bearing based on intrinsic feature-scale decomposition (ICD) and optimization quality factor is proposed in order to solve the problem of extremely weak fault feature in the early stage of rolling bearing. In order to reduce the interference of noise, an ICD decomposition method is proposed on the basis of summarizing the advantages of local mean decomposition (LMD) method and current time scale decomposition (ITD) method. The decomposition performance of ICD and LMD was compared with simulation signal. The results show that the ICD has higher decomposition precision and computational efficiency, and is more suitable for decomposing the vibration signals of rolling bearings. Aiming at the problem that the quality factor Q is difficult to be determined in the resonance sparse decomposition, the preference of the characteristic frequency ratio (CFR) to carry out the quality factor Q is introduced, and the optimal high-quality factor QH and the low-quality factor QL are found according to the maximum CFR value. Combining ICD with ORSSD, an identification method based on ICD and ORSSD for early fault location of rolling bearing is presented. Through simulation and experimental signal testing, this method can extract early fault features effectively and complete the early fault diagnosis of rolling bearing.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TH133.33
本文編號：2312341
[Abstract]:Rolling bearing is one of the widely used components in rotating machinery, and its safe operation is of vital importance to the whole equipment. On the basis of summarizing the existing fault diagnosis technology of rolling bearing, this paper uses modern signal processing technology to study the feature extraction, early fault detection and early fault location identification of rolling bearing. The main contents of this paper are as follows: (1) The feature extraction method based on multi-scale symbol dynamic entropy (MSDE) based on multi-scale symbol dynamic entropy (MSDE) is presented in this paper. Based on the symbol dynamic filtering (SDF), the symbol dynamic entropy constructs a state pattern matrix and a state transition matrix of the symbol sequence, thereby greatly preserving the fault information of the vibration signal. Through the four simulation signal tests, it is verified that the symbolic dynamics entropy can effectively distinguish the change of signal dynamics and has higher computational efficiency. According to the concept of multi-scale analysis, a feature extraction method of MSDE is proposed, which can describe the complexity of signal at different scales. At the same time, the maximum correlation minimum redundancy is used to carry out pattern recognition and the least two-multiplication support vector machine to carry out pattern recognition, and the method has higher recognition precision and calculation efficiency based on the feature extraction method of the MSDE and realizes accurate discrimination of different fault positions of the rolling bearing. (2) Aiming at the problem of insufficient feature extraction ability of multi-scale fuzzy entropy (MFE) on fault damage degree of rolling bearing, a method of feature extraction based on hierarchical fuzzy entropy (HFE) is proposed. Aiming at the disadvantage of multi-scale analysis in MFE, a method for feature extraction of rolling bearing based on HFE is proposed. The hierarchical analysis can extract fault information hidden from low and high frequency parts of the signal at the same time. Through the test of white noise and pink noise, HFE can describe the change of time sequence complexity more accurately and comprehensively, and has better stability. then the characteristic is optimized by using the Laplacian value, and the recognition of different degrees of fault of the rolling bearing is realized by combining the binary tree support vector machine. Compared with MFE method, it is shown that HFE has good separability based on the feature vector extracted by HFE, which realizes the accurate differentiation of different fault degree of rolling bearing. (3) In order to realize the early fault detection of rolling bearing, the performance monitoring index M based on SDF is put forward. The calculation process of the monitoring index M is independent of the fault sample data of the bearing, and compared with the effective value and the kurtosis factor, The monitoring index M has high sensitivity and stability to early bearing failure. The monitoring index M is combined with the accumulation and the combination to complete the early fault alarm of the bearing, and the test of the full life experiment is accelerated through two bearings, and the method can effectively detect the early failure of the bearing. (4) The early fault diagnosis method of rolling bearing based on intrinsic feature-scale decomposition (ICD) and optimization quality factor is proposed in order to solve the problem of extremely weak fault feature in the early stage of rolling bearing. In order to reduce the interference of noise, an ICD decomposition method is proposed on the basis of summarizing the advantages of local mean decomposition (LMD) method and current time scale decomposition (ITD) method. The decomposition performance of ICD and LMD was compared with simulation signal. The results show that the ICD has higher decomposition precision and computational efficiency, and is more suitable for decomposing the vibration signals of rolling bearings. Aiming at the problem that the quality factor Q is difficult to be determined in the resonance sparse decomposition, the preference of the characteristic frequency ratio (CFR) to carry out the quality factor Q is introduced, and the optimal high-quality factor QH and the low-quality factor QL are found according to the maximum CFR value. Combining ICD with ORSSD, an identification method based on ICD and ORSSD for early fault location of rolling bearing is presented. Through simulation and experimental signal testing, this method can extract early fault features effectively and complete the early fault diagnosis of rolling bearing.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TH133.33
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